dfd

Initialize

gitflow	git
`git flow init`	`git init`
	`git commit --allow-empty -m "Initial commit"`
	`git checkout -b develop master`

Connect to the remote repository

Rich Hickey on becoming a better developer

Rich Hickey • 3 years ago

Sorry, I have to disagree with the entire premise here.

A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.

Mastery comes from a combination of at least several of the following:

Git Advanced Resources

Interactive Rebase

Enter interactive rebase using git rebase -i
Some rebase options include:
- squash: combine commits
- edit: split commits (using git reset HEAD^)
- reword: rename commit
- pick: run commits in order

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	I recently found myself in need of a function to sample randomly from an arbitrarily defined probability density function. An excellent post by Quantitations shows how to accomplish this using some of Rs fairly sophisticated functional approximation tools such as integrate and uniroot. The only problem with this excellent post was that the machine cost was enormous with samples of 1000 draws taking 10 seconds on my machine and repeated samples of 100,000+ draws (which I was after) clearly being unworkable.

	Thus I decided to take my own crack at it. First let us review the basics of drawing random variables from non-uniform distributions. The standard method I think most algorithms use works as follows:

	Assumptions
	1. You can draw pseudo-random uniform variable u
	2. You can integrate the pdf to construct a cdf
	$$p = F(x) = \int_{-\infty}^\infty f(x) dx$$
	3. You can invert the cdf in order to solve for p
	$$G(F(x))=F^{-1}(F(x))=F^{-1}(p)=x$$

	import seaborn as sns
	from scipy.optimize import curve_fit

	# Function for linear fit
	def func(x, a, b):
	return a + b * x

	# Seaborn conveniently provides the data for
	# Anscombe's quartet.
	df = sns.load_dataset("anscombe")